High-quality de novo assembly of the Eucommia ulmoides haploid genome provides new insights into evolution and rubber biosynthesis

We report the acquisition of a high-quality haploid chromosome-scale genome assembly for the first time in a tree species, Eucommia ulmoides, which is known for its rubber biosynthesis and medicinal applications. The assembly was obtained by applying PacBio and Hi–C technologies to a haploid that we specifically generated. Compared to the initial genome release, this one has significantly improved assembly quality. The scaffold N50 (53.15 MB) increased 28-fold, and the repetitive sequence content (520 Mb) increased by 158.24 Mb, whereas the number of gaps decreased from 104,772 to 128. A total of 92.87% of the 26,001 predicted protein-coding genes identified with multiple strategies were anchored to the 17 chromosomes. A new whole-genome duplication event was superimposed on the earlier γ paleohexaploidization event, and the expansion of long terminal repeats contributed greatly to the evolution of the genome. The more primitive rubber biosynthesis of this species, as opposed to that in Hevea brasiliensis, relies on the methylerythritol-phosphate pathway rather than the mevalonate pathway to synthesize isoprenyl diphosphate, as the MEP pathway operates predominantly in trans-polyisoprene-containing leaves and central peels. Chlorogenic acid biosynthesis pathway enzymes were preferentially expressed in leaves rather than in bark. This assembly with higher sequence contiguity can foster not only studies on genome structure and evolution, gene mapping, epigenetic analysis and functional genomics but also efforts to improve E. ulmoides for industrial and medical uses through genetic engineering

Identification of key ferroptosis-related biomarkers in steroid-induced osteonecrosis of the femoral head based on machine learning

Abstract Background This study was aimed to identify key ferroptosis-related biomarkers in steroid-induced osteonecrosis of the femoral head (SONFH) based on machine learning algorithm. Methods The SONFH dataset GSE123568 (including 30 SONFH patients and 10 controls) was used in this study. The differentially expressed genes (DEGs) were selected between SONFH and control groups, which were subjected to WGCNA. Ferroptosis-related genes were downloaded from FerrDb V2, which were then compared with DEGs and module genes. Two machine learning algorithms were utilized to identify key ferroptosis-related genes, and the underlying mechanisms were analyzed by GSEA. Correlation analysis between key ferroptosis-related genes and immune cells was analyzed by Spearman method. The drug–gene relationships were predicted in CTD. Results Total 2030 DEGs were obtained. WGCNA identified two key modules and obtained 1561 module genes. Finally, 43 intersection genes were identified as disease-related ferroptosis-related genes. After LASSO regression and RFE-SVM algorithms, 4 intersection genes (AKT1S1, BACH1, MGST1 and SETD1B) were considered as key ferroptosis-related gene. The 4 genes were correlated with osteoclast differentiation pathway. Twenty immune cells with significant differences were obtained between the groups, and the 4 key ferroptosis-related genes were correlated with most immune cells. In CTD, 41 drug–gene relationship pairs were finally obtained. Conclusions The 4 key ferroptosis-related genes, AKT1S1, BACH1, MGST1 and SETD1B, were identified to play a critical role in SONFH progression through osteoclast differentiation and immunologic mechanisms. Additionally, all the 4 genes had good disease prediction effect and could act as biomarkers for the diagnosis and treatment of SONFH

Magnetic resonance imaging features for the differential diagnosis of local recurrence of bone sarcoma after prosthesis replacement

Objective: To explore the imaging features of local recurrences (LRs) based on magnetic resonance imaging (MRI) after oncological orthopaedic surgery with prosthesis reconstruction. Methods: A total of 78 cases totalling 157 scans were retrospectively reviewed. Patients with nodule/mass-like signals were retrospectively classified into LR, infectious pseudotumour, and asymptomatic pseudotumour according to clinicopathological data. LRs were histologically confirmed, and the patients without recurrences were followed up for at least 2 years. Mass size distribution and radiological characteristics were analysed for differential diagnosis of the LR versus pseudotumour. Results: Thirty-three of 78 cases were positive with nodule/mass-like signal findings on the post-operative MRI images. By analysing the size distribution, we found that masses >2.1 cm (14) were almost attributable (98% specificity) to LRs and mostly (84.6%) timely treated. Contrarily, masses ≤2.1 cm (19) are challenging for differential diagnosis of LRs versus pseudotumour and were undertreated in five of the nine LR cases. MRI characteristics of masses ≤2.1 cm were found to be highly heterogeneous, with solid appearance, adjacent infiltration, and less peritumour oedema being significant indicators for LRs (P＜0.05). Receiver operating characteristic curve showed area under curve of 0.93 for this predictive model. Conclusions: For the post-operative MRI surveillance of oncological orthopaedic surgery with prosthesis reconstruction, a mass larger than 2.1 cm was highly specific for recurrence. When a mass was smaller than 2.1 cm, more solid property, more adjacent tissue infiltration, and less muscular oedema indicated recurrence rather than a benign mass. The translational potential of this article: There has been very little data associated with the post-operative magnetic resonance imaging features indicating recurrence in patients with malignant bone sarcoma after prosthesis replacement. This study could help develop diagnostic features of magnetic resonance imaging for differentiating recurrence from benign changes in these patients after prosthesis replacement. Keywords: Magnetic resonance imaging, Post-operative, Prosthesis, Recurrenc

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis

Background: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. Methods: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. Results: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. Conclusions: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions

Gastric Cancer Staging with Dual Energy Spectral CT Imaging

<div><p>Purpose</p><p>To evaluate the clinical utility of dual energy spectral CT (DEsCT) in staging and characterizing gastric cancers.</p><p>Materials and Methods</p><p>96 patients suspected of gastric cancers underwent dual-phasic scans (arterial phase (AP) and portal venous phase (PP)) with DEsCT mode. Three types of images were reconstructed for analysis: conventional polychromatic images, material-decomposition images, and monochromatic image sets with photon energies from 40 to 140 keV. The polychromatic and monochromatic images were compared in TNM staging. The iodine concentrations in the lesions and lymph nodes were measured on the iodine-based material-decomposition images. These values were further normalized against that in aorta and the normalized iodine concentration (nIC) values were statistically compared. Results were correlated with pathological findings.</p><p>Results</p><p>The overall accuracies for T, N and M staging were (81.2%, 80.0%, and 98.9%) and (73.9%, 75.0%, and 98.9%) determined with the monochromatic images and the conventional kVp images, respectively. The improvement of the accuracy in N-staging using the keV images was statistically significant (p<0.05). The nIC values between the differentiated and undifferentiated carcinoma and between metastatic and non-metastatic lymph nodes were significantly different both in AP (p = 0.02, respectively) and PP (p = 0.01, respectively). Among metastatic lymph nodes, nIC of the signet-ring cell carcinoma were significantly different from the adenocarcinoma (p = 0.02) and mucinous adenocarcinoma (p = 0.01) in PP.</p><p>Conclusion</p><p>The monochromatic images obtained with DEsCT may be used to improve the N-staging accuracy. Quantitative iodine concentration measurements may be helpful for differentiating between differentiated and undifferentiated gastric carcinoma, and between metastatic and non-metastatic lymph nodes.</p></div

Same patient as Figure 3.

<p>Monochromatic image in portal phase demonstrated striation enhancement of blurring and wide reticular strands surrounding the outer border (arrow heads) of the tumor staged as T3 which was proved by histology.</p

Receiver operating characteristic curves for differentiating metastatic and non-metastatic lymph node in portal phase.

<p>Receiver operating characteristic curves for differentiating metastatic and non-metastatic lymph node in portal phase.</p